Out-of-hospital cardiac arrest (OHCA) strikes over 300,000 people in the US annually. Between 30-50% of OHCA survivors experience some degree of long-term neurologic injury, from mild cognitive defects to severe disability. These injuries are the result of a complex set of ischemia-reperfusion phenomena, known as the post-cardiac arrest syndrome (PCAS), that occurs over the first few days after resuscitation. Prognostication during this critical period, and prediction of neurologic disability, remains an enormous challenge. Serologic markers of brain injury, such as nerve-specific enolase or S100-B, have been tested extensively but have limited predictive value. Quantifying PCAS injury during clinical care has remained a key knowledge gap; with more accurate injury measurement, clinicians would be better able to tailor patient care appropriately by adjusting the intensity of brain-protective therapies including targeted temperature management. Our team has developed a novel approach to injury assessment that applies nanofluidic technology to isolate and analyze brain-derived extracellular vesicles (EVs) from serum, small circulating bodies that represent their cells of origin and are released into the blood in greater quantity when cells are damaged or die. Exosomes carry a collection of microRNA (miRNA) species that can be measured and sequenced, allowing for precise analysis of a ?transcriptome of brain injury?. When we coupled brain-derived exosomal miRNA analysis with machine learning techniques, we demonstrated that exosomal measurement predicted injury severity after traumatic brain injury. We now seek to apply this technology to post-arrest neurologic assessment; we hypothesize that serial EV sampling will allow for more precise measurement of PCAS-related brain injury. We will perform serial blood sampling of adult (age>17 y) non-traumatic resuscitated OHCA patients (0,6,12,24 h post-arrest) as well as from a negative control set of patients with myocardial infarction and positive control set of patients with significant cerebrovascular accident. We will characterize brain-derived miRNA species from both cohorts, first in a ?derivation group? (n=10) and then a ?validation group? (n=40) for each cohort. We will collect demographic and clinical outcome data, including neurologic status at discharge and at 90 d via modified Rankin Scale (mRS), dichotomized into ?good? (mRS 0-2) or ?poor? (mRS 3-6) outcome. A machine learning algorithm will be applied to the derivation group to develop an injury predictive tool, which will then be tested in the validation group. We hypothesize that (1) EV-derived miRNA will identify OHCA patients with brain injury at discharge compared to negative controls and (2) miRNA analysis will discriminate between OHCA patients with good or bad outcomes. Finally, we will apply the predictive tool across 4 timepoints in the OHCA cohort, hypothesizing that more injured patients will show a more significant injury miRNA pattern among earlier timepoints. If successful, this strategy could be coupled to post-arrest trials that require real-time neurologic injury evaluation, and could enable clinicians to tailor care to improve long-term neurologic outcomes.